Keras 混淆矩阵值错误：找到样本数不一致的输入变量：[3，360]

我试图训练一个数据集，并在训练完数据集后输出一个混淆矩阵

这是密码

import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator 

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import Input

from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Softmax
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.layers import Convolution2D

import os
import numpy as np
import matplotlib.pyplot as plt

import scipy as sp 
from scipy import signal
from scipy.signal import chirp
import numpy.fft
from numpy.fft import fft as rf
import random
import pandas as pd
import sklearn.model_selection as model_selection
import matplotlib.pyplot as plt
import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator 

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import Input

from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Softmax
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.layers import Convolution2D

import os
import numpy as np
import matplotlib.pyplot as plt

import scipy as sp 
from scipy import signal
from scipy.signal import chirp
import numpy.fft
from numpy.fft import fft as rf
import random
import pandas as pd
import sklearn.model_selection as model_selection
import matplotlib.pyplot as plt

from sklearn.datasets import make_blobs
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import brier_score_loss
from sklearn.calibration import CalibratedClassifierCV
from sklearn.model_selection import train_test_split

from sklearn.datasets import make_blobs
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import brier_score_loss
from sklearn.calibration import CalibratedClassifierCV
from sklearn.model_selection import train_test_split

from PIL import Image
import imageio as io 
import glob
from matplotlib import image

  
import h5py
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Activation, concatenate
from tensorflow.keras.layers import Flatten, Dropout
from tensorflow.keras.layers import Convolution2D, MaxPooling2D
from tensorflow.keras.layers import AveragePooling2D

from tensorflow.keras.layers import Input, Conv2D, Concatenate, \
     MaxPool2D, GlobalAvgPool2D, Activation

def squeezenet(input_shape, n_classes):
  
  def fire(x, fs, fe):
    s = Conv2D(fs, 1, activation='relu')(x)
    e1 = Conv2D(fe, 1, activation='relu')(s)
    e3 = Conv2D(fe, 3, padding='same', activation='relu')(s)
    output = Concatenate()([e1, e3])
    return output
  
  
  input = Input(input_shape)
  
  x = Conv2D(96, 7, strides=2, padding='same', activation='relu')(input)
  x = MaxPool2D(3, strides=2, padding='same')(x)
  
  x = fire(x, 16, 64)
  x = fire(x, 16, 64)
  x = fire(x, 32, 128)
  x = MaxPool2D(3, strides=2, padding='same')(x)
  
  x = fire(x, 32, 128)
  x = fire(x, 48, 192)
  x = fire(x, 48, 192)
  x = fire(x, 64, 256)
  x = fire(x, 64, 256)
  x = MaxPool2D(3, strides=2, padding='same')(x)
    
  x = Dropout(0.6)(x)
    
  x = Conv2D(n_classes, 1)(x)
  x = GlobalAvgPool2D()(x)
  x = Flatten()(x)
  
  output = Activation('softmax')(x)
  
  model = Model(input, output)
  return model

  import pathlib
import PIL

test_datagen = ImageDataGenerator(rescale=1./255)


data_dir = os.path.join(r"location/directory of the file", "file")

data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*.png')))
print(image_count)

rect = list(data_dir.glob('Rect/*'))
PIL.Image.open(str(rect[1]))


batch_size = 32
img_height = 227
img_width = 227

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.1,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.1,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

class_names = train_ds.class_names
print(range(len(class_names)))

AUTOTUNE = tf.data.experimental.AUTOTUNE

train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)

normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
# Notice the pixels values are now in `[0,1]`.
print(np.min(first_image), np.max(first_image)) 

from keras.optimizers import SGD
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator


model = squeezenet((227,227,3),2)

sgd = SGD(lr=0.001, decay=0.0002, momentum=0.9, nesterov=True)
model.compile(
optimizer=sgd, loss='binary_crossentropy', metrics=['accuracy'])

print(model.summary())

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss=history.history['loss']
val_loss=history.history['val_loss']

epochs_range = epoch #range(epochs)

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss=history.history['loss']
val_loss=history.history['val_loss']

epochs_range = epoch #range(epochs)

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

#supply a imafe to classifer to get an image out 
#calculate the confusion matrix manualy 

from sklearn.metrics import classification_report, confusion_matrix


Y_pred = model.predict_generator(val_ds, 720 // 32+1)
y_pred = np.argmax(Y_pred, axis=1)
print(y_pred.shape)
print('Confusion Matrix')
print(confusion_matrix(class_names, y_pred))
print('Classification Report')
target_names = ['Cats', 'Dogs', 'Horse']
print(classification_report(class_names, y_pred, target_names=target_names))

这是我得到的错误

ValueError                                Traceback (most recent call last)
<ipython-input-9-5188ce05905a> in <module>
      9 print(y_pred.shape)
     10 print('Confusion Matrix')
---> 11 print(confusion_matrix(class_names, y_pred))
     12 print('Classification Report')
     13 target_names = ['Cats', 'Dogs', 'Horse']

in confusion_matrix(y_true, y_pred, labels, sample_weight)
    251 
    252     """
--> 253     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
    254     if y_type not in ("binary", "multiclass"):
    255         raise ValueError("%s is not supported" % y_type)

in _check_targets(y_true, y_pred)
     69     y_pred : array or indicator matrix
     70     """
---> 71     check_consistent_length(y_true, y_pred)
     72     type_true = type_of_target(y_true)
     73     type_pred = type_of_target(y_pred)

in check_consistent_length(*arrays)
    203     if len(uniques) > 1:
    204         raise ValueError("Found input variables with inconsistent numbers of"
--> 205                          " samples: %r" % [int(l) for l in lengths])
    206 
    207 

ValueError: Found input variables with inconsistent numbers of samples: [3, 360]

如您所能找到的，混乱矩阵方法中的参数应该是y_true和y_pred

第一个参数y_true=class_names的大小为3，第二个参数y_pred的大小为360。但是，两者的大小应该相同，因为y_pred是分类的估计结果，y_true是分类的基本事实

下面是使用3个类的sklearn关联示例：[0、1、2]

从sklearn.metrics导入混淆矩阵 y_true=[2,0,2,2,0,1] y_pred=[0,0,2,2,0,2] 混乱是真的，是的 编辑

y_真是如何构造的

---

通常y_true包含与每个输入对应的每个标签。您似乎为分类器提供了360个输入，因此每个输入都应该有一个关联的标签，一个类名。包含输入的真实标签的完整向量是y\u true。

在这种情况下，y\u true应该是什么？我更新了问题：y\u true应该与y\u pred大小相同。y_true包含预期结果的真值。我想另一个问题是，因为我的验证大小是360。这就是我正在接受的训练，那么我将如何创建Y真以使其与我的混淆矩阵一起工作呢？我刚刚更新了问题，给出了如何构建Y真的详细信息。希望这有助于消除大量的。。。重复进口，以及与手头问题无关的物品等；明白为什么了吗